The op `aten.rand_like` was missing a shape function, unit tests, and
the `dtype` argument was being ignored in its decomposition. This
commit fixes all three things.
A user might want to avoid the extra layer of multiprocessing libary for
debugging purpose. In such cases, the -s flag can be used to force
sequential execution.
This commit adds support for aten.max_pool2d, aten.max_pool2d_with_indices,
and aten.avg_pool2d op for the cases where ceil_mode = true.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The preserve memory specifies that `If any of the input tensors is in channels_last format,
operator output should be in channels_last format` and hence can be
added as is in aten_empty_like op.
This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`,
`aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py and scalar_comparison.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
- This commit adds support for `aten.mean.dim` op.
- It also adds a new test script `stats.py` for statistics related ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit decomposes `aten.to.dtype_layout` op into `aten.to.dtype` op.
This commit also fixes the formatting for the file type_conversion.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds lowering of `aten.masked_fill.Scalar` op.
This commit also fixes the formatting of the file constant_alloc.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit fixes the `ConstantPad2dStaticModule` test case by adding
the lowering of `aten.pad` operation. Previously the test case
mapped to `aten.constant_pad_nd` operation.
The `aten.pad` now decomposes into `aten.constant_pad_nd` operation.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
1. This commit adds lowering of "while-like" prim loop to scf.while
operation.
2. Adds lowering of "for-like" prim loops to scf.for operation.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
Fix the inplace update tensor issue we had
where the torchscript execution would update the input value inplace
resulting the actual test not being able to see the original input
value.
This commit adds more test cases `aten::index_put` op.
This commit also fixes formatting issues with the test file index_put.py
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds lowering of `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The updated LLVM code includes a patch to create bfloat16 array
attributes, thus enabling a different patch to torch-mlir to flesh out
support for the bfloat16 type.
Added the dynamic registration of return function to the execution
engine. This makes sure that different/multiple return types are supported.
Also, updated the .style.yapf indentation to 4.
This makes it much easier to convert models and hides all the
ClassAnnotator complexity.
This also adds a new example `torchscript_resnet18_all_output_types.py`
which shows the ResNet18 IR for all output types.
Also,
- This moves `run_pipeline_with_repro_report` to
`torch_mlir.compiler_utils`.
That way, downstreams don't have to duplicate this list.
Also, remove "external config" feature, since it is subsumed by just
importing the test suite.
This commit adds lowering of `aten::max_pool2d_with_indices_backward` op.
This commit also fixes formatting issues in basic.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds the following support to the op `nll_loss_backward`:
- `input` tensor can be rank-1
- `weight` parameter
- `reduction` parameter
- `target`, `grad_output`, `total_weight` can be rank-0
- Checks that input tensors are of the expected type
This commit adds support for multi-dimensional tensors as input to the
`_index_put_impl_` op. The support was to some degree already there,
since `ScatterOp` already supports multi-dimensional tensors. This
commit also adds a bit more error checking to `index_put` and
refactors the code for creating `ScatterOp`s to mimic the way one
would make a `Linalg::GenericOp`.
This commit decomposes different variants of `aten.where.*` op into
`aten.where.Self` op. It covers `aten.where.Scalar`,
`aten.where.ScalarSelf` and `aten.where.ScalarOther` ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit decomposes `aten.new_empty` op into `aten.empty.memory_format` op.
This commit also made a dtype fix to the constant tensor allocation like ops.
Earlier the dtype for the result was inferred from the result type; now, it's
being evaluated as per the original definition of the op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
Effectively, this mode works by compiling op by op as the NN is eagerly executed by PyTorch. Entailed in that compilation is building a representation of the op that can be `torch.jit.script`ed, importing using `ModuleBuilder`, and then executing (e.g., with `RefBackendLinalgOnTensorsBackend`). This mode includes a fallback to conventional PyTorch if anything in the torch-mlir compilation process fails (e.g., unsupported op).
Currently, all e2e tests pass execpt for two that involve an upstream PyTorch bug (https://github.com/pytorch/pytorch/issues/74400).
High priority next steps:
1. A compile cache in order to speed up reruns of the same NN.
2. Integration with IREE (though not in this repo).
3. Integration with `torch.distributed`.
Ramiro Leal-Cavazos
Yi Zhang
Vivek Khandelwal
Prashant Kumar
Gaurav Shukla
gpetters94
Prateek Gupta
Sean Silva
Ashay Rane
Maksim Levental
Vigilans